The present embodiments relate to volume rendering. In particular, medical data is volume rendered.
Volume rendering is a general method to composite 3D digital volumetric data onto a 2D image. The quality and appearance of the resulting image can vary widely from one volume rendering engine to another due to the choice of different tradeoffs in different implementations. Even within one volume rendering engine, different image quality and appearance can be produced depending on the choice of parameters. Other than gross errors, there is often no right or wrong resulting image, only whether the resulting image is “better” or “worse” in revealing the desired features for a particular task or in minimizing the appearance of undesirable rendering artifacts.
The choice of rendering engine and parameters is often left up to the subjective heuristics of the software developers, who attempt to select parameters that would yield good quality given rendering speed performance considerations. A volume rendering engine is capable of producing a range of image qualities that are inversely related to the rendering speed. Faster rendering methods often rely on simpler algorithms and approximations that can introduce visible distortions or structural artifacts that degrade image quality.
Common volume rendering artifacts include shading edge noise, edge non-smoothness, striations, and opacity inconsistency. Shading noise is dark and light grains and often occurs in regions of the data where a surface is ill defined. Small noise levels in the data are exaggerated in the shading computation. Shading noise artifacts are common among volume rendering engines that use gradient-based shading methods without special surface treatments. Edge non-smoothness often occurs when a slice-based volume is clipped by a binary mask in an orientation that is not orthogonal to the volume axes. Striation artifacts may result from various sources such as under-sampling, the choice of filtering kernel, quantization, or classification schemes. Opacity inconsistency occurs when a volume renderer fails to account fully for the size variations in anisotropic volume data, resulting in variations in visible image color and/or luminance as the volume is rotated.
The tradeoffs between quality and rendering speed are evaluated through subjective visual assessments by rendering engine developers. Conventional distortion metrics, such as mean squared error, have limited utility because they often fail over a range of image and artifact characteristics. Mean squared error does not provide a meaningful quantitative reference since its value highly depends on the content of the image.